Improved Monarch Butterfly Optimization for Multi-to-Multi Weapon-Target Assignment Problems

Abstract

This article introduces an improved monarch butterfly optimization (IMBO) algorithm to solve a multi-to- multi weapon-target assignment (WTA) problem. First, the proposed WTA model uses the threat evaluation model and interception performance model to obtain a weapon assignment criterion. Second, to ensure that each target is allocated appropriate interception resources, a penalty function is used to treat the constraints. Then, the proposed algorithm solves the problems of slow convergence and low accuracy by using a greedy strategy migration operation and an adaptive crossover operator. The IMBO algorithm is validated for the WTA problem and is compared with monarch butterfly optimization (MBO), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. The experimental results indicate that the IMBO algorithm is clearly superior to the standard MBO, PSO, and ABC algorithms.